Golf is one of most rapidly growing sports in the world, and relevant industries such as indoor driving ranges, screen golf clubs and golf games are in increasing demand. Despite of development of relevant industries represented by golf sports and content infrastructures, it is not easy to improve golf skill. This is not caused by personal physical differences such as heights or weights as in basketball or soccer but caused by complicated golf swing mechanism composed of fixing of eyes, shift of center of gravity, swing trajectory or the like.
In order to improve the swing mechanism, golf players repeatedly practice their golf swing by themselves or adjust their golf swing with the assistance of a professional coach. However, even though a golf player practices golf swing by himself, he cannot easily recognize points to improve. In addition, the adjustment with the assistance of a coach may not be easily associated with feeling or mechanism of actual swing, and the appropriate swing mechanism may not be maintained after the correction. Thus, the swing of the golf player may return to his original swing. To overcome this problem, there is a demand for a system for automatically analyzing golf swing of a user and suitably feeding digitized and visualized information back to the user.
In order to realize an automatic system for analyzing golf swings, there are various methodologies proposed for tracking the motion of a specific body part at successively input swings of a user. The methods being studied for tracking a body part in the art may be classified into a marker-based method and a marker-less method, depending on the presence of a marker.
In the marker-based method, a marker is attached to a part of a human body to be tracked and the motion of the marker during swing is detected and analyzed. Meanwhile, in the marker-less method, a single camera or a plurality of cameras are installed and the part of the human body is tracked by analyzing images received from the cameras.
An existing marker-based tracking method which tracks a body part by analyzing golf swing may sense the motion of the body part in a three-dimensional space by tracking a marker attached to the body of a user. However, this method needs installation of an expensive instrument for tracking the marker and may cause discomforts to the user during his/her swing due to the attached marker.
Recently, in order to overcome the drawbacks of the marker-based system, a marker-less method for tracking a body part by receiving and analyzing swing of a user through a single camera or a plurality of cameras without attaching a marker is attracting attention. This system may use image data or depth data, depending on the type of the camera used.
In the marker-less method, a system which receives swing of a user through an image camera is attracting great attention since image cameras recently become popular and thus various smart phone applications in relation to golf swing have appeared. However, there is limitation in tracking a body part moving in a three-dimensional space by using two-dimensional image data (for example, when various body parts overlaps each other), and the result of tracking the body part is unstable due to noise or sensitiveness to brightness of light.
In order to overcome the drawbacks of the image camera-based system, a body part tracking method using a depth camera is on the rise. The depth camera outputs an image including distance information to a pixel, namely three-dimensional information of x-, y- and z-axes. Differently from color or brightness information of an image camera, the depth camera gives intuitive information for a structure of an object in the image and outputs stable data not sensitive to the brightness.
Studies and methods in relation to depth camera-based body part tracking for analyzing golf swing have not been proposed. However, methods for tracking a general motion are actively being studied. Representatively, there are XBOX 360 Kinect Project of Microsoft and OpenNI of Prime Sense. Kinect of Microsoft is a probabilistic model-based system and trains a classifier with features of a body part to be tracked. If an unknown depth image is input, Kinect calculates probability of each pixel of the image through the classifier to estimate the body part. OpenNI of Prime Sense tracks a body part by structurally analyzing a person present in an input depth image. The above studies show stable results of body part tracking in comparison to existing studies for general motions, but they are not suitable for special motions such as golf swing analysis. For example, hands during golf swing at address may not be accurately tracked, and if the head moves close to the hands during back swing, the head and the hands may be confused.
U.S. Patent Publication No. 2010/0034457 A1 discloses a method for identifying the trunk, the head and the limbs of a person by using a depth image, which however may provide unstable identification results since the trunk, the head and the limbs are distinguished using certain proportions.